|DenseNet-169|23.01|6.76|14,149,480|3,403.89M|Converted from GL model ([log](https://github.com/osmr/imgclsmob/releases/download/v0.0.402/densenet169-0676-417877ef.pth.log))| |DenseNet-169|22.42|6.29|14,149,480|3,403.89M|Converted from GL model ([log](https://github.com/osmr/img...
model.py 整体为 1.输入：图片2.经过feature block（图中的第一个convolution层，后面可以加一个pooling层,这里没有画出来）3.经过第一个dense block， 该Block中有n个dense layer,灰色圆圈表示，每个dense layer都是dense connection,即每一层的输入都是前面所有层的输出的拼接4.经过第一个transition block,由...
r"""Densenet-169 model from `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet """ model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32)...
model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16), **kwargs) return model def densenet169(**kwargs): model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32), **kwargs) return model def densenet201(**kwargs): m...
model = Model(inputs=inputs, outputs=x, name='densenet169')elifdense_blocks_num_list == [6,12,48,32]: model = Model(inputs=inputs, outputs=x, name='densenet201')else: model = Model(inputs=inputs, outputs=x, name='densenet') ...
以Resnet18为例,在程序中输入 from __future__ import print_function, division from torchvision import models model_ft = models.resnet18(pretrained=True) 然后运行,就会出
r"""Densenet-169 model from `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr memory_efficient (bool)...
模型名称(Model name):内置了121、161、169和201.一般我们可以选择201或者161. 预训练(Pretrained):指定是否使用在 ImageNet 上预先训练的模型。建议选择True。 内存效率(Memory efficient):指定是否使用检查点,检查点的内存效率要高得多,但速度较慢。模式是False。
return model def DenseNet121(input_shape=[224,224,3], classes=1000, **kwargs): return DenseNet([6, 12, 24, 16], input_shape, classes, **kwargs) def DenseNet169(input_shape=[224,224,3], classes=1000, **kwargs): return DenseNet([6, 12, 32, 32], ...
model_urls = { 'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth', 'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth', 'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth', ...